Data-driven phylodynamics: molecular evolution to epidemiology. This project aims to uncover how different environmental and ecological variables drive the emergence of pathogens with increased transmissibility or virulence, known as variants. This will be achieved through extensive analyses of virus genome data.
This project expects to generate new knowledge in the field of pathogen evolution using novel data-driven statistical techniques for genomic analyses.
Expected outcomes of this proje ....Data-driven phylodynamics: molecular evolution to epidemiology. This project aims to uncover how different environmental and ecological variables drive the emergence of pathogens with increased transmissibility or virulence, known as variants. This will be achieved through extensive analyses of virus genome data.
This project expects to generate new knowledge in the field of pathogen evolution using novel data-driven statistical techniques for genomic analyses.
Expected outcomes of this project are a new understanding of the circumstances under which pathogen variants emerge and a suite of statistical tools to exploit the vast genome data available.
This should provide significant benefits by generating new knowledge with the potential to improve biosecurity, agriculture, and heath.
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Discovery Early Career Researcher Award - Grant ID: DE240100316
Funder
Australian Research Council
Funding Amount
$435,431.00
Summary
Population genomic methods for modelling bacterial pathogen evolution. This project aims to develop novel techniques to model bacterial genome evolution and improve our understanding of how major agricultural and human pathogens, including Enterococcus, Salmonella and E. coli, evolve. The project expects to generate new knowledge about how horizontal gene transfer shapes the evolution of bacteria and how these dynamics vary over different temporal scales. Expected outcomes include methodological ....Population genomic methods for modelling bacterial pathogen evolution. This project aims to develop novel techniques to model bacterial genome evolution and improve our understanding of how major agricultural and human pathogens, including Enterococcus, Salmonella and E. coli, evolve. The project expects to generate new knowledge about how horizontal gene transfer shapes the evolution of bacteria and how these dynamics vary over different temporal scales. Expected outcomes include methodological advances that will enable the analysis of massive contemporary datasets. These methods and resulting analyses will provide significant benefits including informing the design of superior long-term interventions to reduce bacterial disease in both agriculture and health that are robust to the evolution of bacteria.Read moreRead less